5 Duties to Automate Utilizing Scheduled Question Lambdas in Rockset

Why and what to automate

As utility builders and designers, at any time when we see repeating duties, we instantly take into consideration learn how to automate them. This simplifies our each day work and permits us to be extra environment friendly and centered on delivering worth to the enterprise.

Typical examples of repeating duties embrace scaling compute sources to optimize their utilization from a price and efficiency perspective, sending automated e-mails or Slack messages with outcomes of a SQL question, materializing views or doing periodic copies of information for improvement functions, exporting knowledge to S3 buckets, and so forth.

How Rockset helps with automation

Rockset gives a set of highly effective options to assist automate widespread duties in constructing and managing knowledge options:

• a wealthy set of APIs so that each facet of the platform might be managed by way of REST
• Question Lambdas – that are REST API wrappers round your parametrized SQL queries, hosted on Rockset
• scheduling of Question Lambdas – a not too long ago launched characteristic the place you may create schedules for computerized execution of your question lambdas and submit outcomes of these queries to webhooks
• compute-compute separation (together with a shared storage layer) which permits isolation and impartial scaling of compute sources

Let’s deep dive into why these are useful for automation.

Rockset APIs will let you work together with your whole sources – from creating integrations and collections, to creating digital situations, resizing, pausing and resuming them, to operating question lambdas and plain SQL queries.

Question Lambdas provide a pleasant and straightforward to make use of solution to decouple customers of information from the underlying SQL queries with the intention to preserve your small business logic in a single place with full supply management, versioning and internet hosting on Rockset.

Scheduled execution of question lambdas lets you create cron schedules that may robotically execute question lambdas and optionally submit the outcomes of these queries to webhooks. These webhooks might be hosted externally to Rockset (to additional automate your workflow, for instance to put in writing knowledge again to a supply system or ship an e-mail), however it’s also possible to name Rockset APIs and carry out duties like digital occasion resizing and even creating or resuming a digital occasion.

Compute-compute separation means that you can have devoted, remoted compute sources (digital situations) per use case. This implies you may independently scale and dimension your ingestion VI and a number of secondary VIs which are used for querying knowledge. Rockset is the primary real-time analytics database to supply this characteristic.

With the mix of those options, you may automate all the pieces you want (besides possibly brewing your espresso)!

Typical use instances for automation

Let’s now have a look into typical use instances for automation and present how you’d implement them in Rockset.

Use case 1: Sending automated alerts

Usually instances, there are necessities to ship automated alerts all through the day with outcomes of SQL queries. These might be both enterprise associated (like widespread KPIs that the enterprise is taken with) or extra technical (like discovering out what number of queries ran slower than 3 seconds).

Utilizing scheduled question lambdas, we will run a SQL question towards Rockset and submit the outcomes of that question to an exterior endpoint resembling an e-mail supplier or Slack.

Let’s take a look at an e-commerce instance. Now we have a group referred to as ShopEvents with uncooked real-time occasions from a webshop. Right here we monitor each click on to each product in our webshop, after which ingest this knowledge into Rockset through Confluent Cloud. We’re taken with realizing what number of gadgets have been bought on our webshop at this time and we wish to ship this knowledge through e-mail to our enterprise customers each six hours.

We’ll create a question lambda with the next SQL question on our ShopEvents assortment:

SELECT
    COUNT(*) As ItemsSold
FROM
    "Demo-Ecommerce".ShopEvents
WHERE 
    Timestamp >= CURRENT_DATE() AND EventType="Checkout";

We’ll then use SendGrid to ship an e-mail with the outcomes of that question. We received’t undergo the steps of establishing SendGrid, you may observe that in their documentation.

When you’ve received an API key from SendGrid, you may create a schedule on your question lambda like this, with a cron schedule of 0 */6 * * * for each 6 hours:

This can name the SendGrid REST API each 6 hours and can set off sending an e-mail with the entire variety of bought gadgets that day.

{{QUERY_ID}} and {{QUERY_RESULTS}} are template values that Rockset gives robotically for scheduled question lambdas with the intention to use the ID of the question and the ensuing dataset in your webhook calls. On this case, we’re solely within the question outcomes.

After enabling this schedule, that is what you’ll get in your inbox:

You might do the identical with Slack API or another supplier that accepts POST requests and Authorization headers and also you’ve received your automated alerts arrange!

Should you’re taken with sending alerts for sluggish queries, take a look at establishing Question Logs the place you may see an inventory of historic queries and their efficiency.

Use case 2: Creating materialized views or improvement datasets

Rockset helps computerized real-time rollups on ingestion for some knowledge sources. Nonetheless, when you have a must create further materialized views with extra advanced logic or if it’s essential have a duplicate of your knowledge for different functions (like archival, improvement of latest options, and many others.), you are able to do it periodically through the use of an INSERT INTO scheduled question lambda. INSERT INTO is a pleasant solution to insert the outcomes of a SQL question into an current assortment (it could possibly be the identical assortment or a totally totally different one).

Let’s once more take a look at our e-commerce instance. Now we have a knowledge retention coverage set on our ShopEvents assortment in order that occasions which are older than 12 months robotically get faraway from Rockset.

Nonetheless, for gross sales analytics functions, we wish to make a copy of particular occasions, the place the occasion was a product order. For this, we’ll create a brand new assortment referred to as OrdersAnalytics with none knowledge retention coverage. We’ll then periodically insert knowledge into this assortment from the uncooked occasions assortment earlier than the information will get purged.

We are able to do that by making a SQL question that can get all Checkout occasions for the day before today:

INSERT INTO "Demo-Ecommerce".OrdersAnalytics
SELECT
    e.EventId AS _id,
    e.Timestamp, 
    e.EventType, 
    e.EventDetails, 
    e.GeoLocation, 
FROM
    "Demo-Ecommerce".ShopEvents e
WHERE 
    e.Timestamp BETWEEN CURRENT_DATE() - DAYS(1) AND CURRENT_DATE()
    AND e.EventType="Checkout";

Be aware the _id discipline we’re utilizing on this question – it will be sure that we don’t get any duplicates in our orders assortment. Try how Rockset robotically handles upserts right here.

Then we create a question lambda with this SQL question syntax, and create a schedule to run this as soon as a day at 1 AM, with a cron schedule 0 1 * * *. We don’t must do something with a webhook, so this a part of the schedule definition is empty.

That’s it – now we’ll have each day product orders saved in our OrdersAnalytics assortment, prepared to be used.

Use case 3: Periodic exporting of information to S3

You should utilize scheduled question lambdas to periodically execute a SQL question and export the outcomes of that question to a vacation spot of your alternative, resembling an S3 bucket. That is helpful for situations the place it’s essential export knowledge regularly, resembling backing up knowledge, creating stories or feeding knowledge into downstream programs.

On this instance, we are going to once more work on our e-commerce dataset and we’ll leverage AWS API Gateway to create a webhook that our question lambda can name to export the outcomes of a question into an S3 bucket.

Much like our earlier instance, we’ll write a SQL question to get all occasions from the day before today, be part of that with product metadata and we’ll save this question as a question lambda. That is the dataset we wish to periodically export to S3.

SELECT
    e.Timestamp, 
    e.EventType, 
    e.EventDetails, 
    e.GeoLocation, 
    p.ProductName, 
    p.ProductCategory, 
    p.ProductDescription, 
    p.Value
FROM
    "Demo-Ecommerce".ShopEvents e
    INNER JOIN "Demo-Ecommerce".Merchandise p ON e.EventDetails.ProductID = p._id
WHERE 
    e.Timestamp BETWEEN CURRENT_DATE() - DAYS(1) AND CURRENT_DATE();

Subsequent, we’ll must create an S3 bucket and arrange AWS API Gateway with an IAM Function and Coverage in order that the API gateway can write knowledge to S3. On this weblog, we’ll deal with the API gateway half – make sure to verify the AWS documentation on learn how to create an S3 bucket and the IAM function and coverage.

Observe these steps to arrange AWS API Gateway so it’s prepared to speak with our scheduled question lambda:

1. Create a REST API utility within the AWS API Gateway service, we will name it rockset_export:

2. Create a brand new useful resource which our question lambdas will use, we’ll name it webhook:

3. Create a brand new POST methodology utilizing the settings beneath – this basically permits our endpoint to speak with an S3 bucket referred to as rockset_export:

• AWS Area: Area on your S3 bucket
• AWS Service: Easy Storage Service (S3)
• HTTP methodology: PUT
• Motion Sort: Use path override
• Path override (optionally available): rockset_export/{question _id} (exchange along with your bucket identify)
• Execution function: arn:awsiam::###:function/rockset_export (exchange along with your ARN function)
• Setup URL Path Parameters and Mapping Templates for the Integration Request – it will extract a parameter referred to as query_id from the physique of the incoming request (we’ll use this as a reputation for information saved to S3) and query_results which we’ll use for the contents of the file (that is the results of our question lambda):

As soon as that’s accomplished, we will deploy our API Gateway to a Stage and we’re now able to name this endpoint from our scheduled question lambda.

Let’s now configure the schedule for our question lambda. We are able to use a cron schedule 0 2 * * * in order that our question lambda runs at 2 AM within the morning and produces the dataset we have to export. We’ll name the webhook we created within the earlier steps, and we’ll provide query_id and query_results as parameters within the physique of the POST request:

We’re utilizing {{QUERY_ID}} and {{QUERY_RESULTS}} within the payload configuration and passing them to the API Gateway which can use them when exporting to S3 because the identify of the file (the ID of the question) and its contents (the results of the question), as described in step 4 above.

As soon as we save this schedule, now we have an automatic process that runs each morning at 2 AM, grabs a snapshot of our knowledge and sends it to an API Gateway webhook which exports this to an S3 bucket.

Use case 4: Scheduled resizing of digital situations

Rockset has help for auto-scaling digital situations, but when your workload has predictable or nicely understood utilization patterns, you may profit from scaling your compute sources up or down based mostly on a set schedule.

That means, you may optimize each spend (so that you just don’t over-provision sources) and efficiency (so that you’re prepared with extra compute energy when your customers wish to use the system).

An instance could possibly be a B2B use case the place your clients work primarily in enterprise hours, let’s say 9 AM to five PM all through the work days, and so that you want extra compute sources throughout these instances.

To deal with this use case, you may create a scheduled question lambda that can name Rockset’s digital occasion endpoint and scale it up and down based mostly on a cron schedule.

Observe these steps:

1. Create a question lambda with only a choose 1 question, since we don’t really want any particular knowledge for this to work.
2. Create a schedule for this question lambda. In our case, we wish to execute as soon as a day at 9 AM so our cron schedule might be 0 9 * * * and we are going to set limitless variety of executions in order that it runs day-after-day indefinitely.
3. We’ll name the replace digital occasion webhook for the particular VI that we wish to scale up. We have to provide the digital occasion ID within the webhook URL, the authentication header with the API key (it wants permissions to edit the VI) and the parameter with the NEW_SIZE set to one thing like MEDIUM or LARGE within the physique of the request.

We are able to repeat steps 1-3 to create a brand new schedule for scaling the VI down, altering the cron schedule to one thing like 5 PM and utilizing a smaller dimension for the NEW_SIZE parameter.

Use case 5: Organising knowledge analyst environments

With Rockset’s compute-compute separation, it’s simple to spin up devoted, remoted and scalable environments on your advert hoc knowledge evaluation. Every use case can have its personal digital occasion, making certain {that a} manufacturing workload stays steady and performant, with the perfect price-performance for that workload.

On this state of affairs, let’s assume now we have knowledge analysts or knowledge scientists who wish to run advert hoc SQL queries to discover knowledge and work on varied knowledge fashions as a part of a brand new characteristic the enterprise desires to roll out. They want entry to collections they usually want compute sources however we don’t need them to create or scale these sources on their very own.

To cater to this requirement, we will create a brand new digital occasion devoted to knowledge analysts, be sure that they will’t edit or create VIs by making a customized RBAC function and assign analysts to that function, and we will then create a scheduled question lambda that can resume the digital occasion each morning in order that knowledge analysts have an atmosphere prepared once they log into the Rockset console. We might even couple this with use case 2 and create a each day snapshot of manufacturing right into a separate assortment and have the analysts work on that dataset from their digital occasion.

The steps for this use case are just like the one the place we scale the VIs up and down:

1. Create a question lambda with only a choose 1 question, since we don’t really want any particular knowledge for this to work.
2. Create a schedule for this question lambda, let’s say each day at 8 AM Monday to Friday and we are going to restrict it to 10 executions as a result of we would like this to solely work within the subsequent 2 working weeks. Our cron schedule might be 0 8 * * 1-5.
3. We are going to name the resume VI endpoint. We have to provide the digital occasion ID within the webhook URL, the authentication header with the API key (it wants permissions to renew the VI). We don’t want any parameters within the physique of the request.

That’s it! Now now we have a working atmosphere for our knowledge analysts and knowledge scientists that’s up and operating for them each work day at 8 AM. We are able to edit the VI to both auto-suspend after sure variety of hours or we will have one other scheduled execution which can droop the VIs at a set schedule.

As demonstrated above, Rockset gives a set of helpful options to automate widespread duties in constructing and sustaining knowledge options. The wealthy set of APIs mixed with the ability of question lambdas and scheduling will let you implement and automate workflows which are utterly hosted and operating in Rockset so that you just don’t should depend on third social gathering parts or arrange infrastructure to automate repeating duties.

We hope this weblog gave you a number of concepts on learn how to do automation in Rockset. Give this a try to tell us the way it works!